Dataset Information

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

ABSTRACT: We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and optimized hyperparameters in order to minimize validation loss. As expected, the resultant PraNet-based deep-learning model outperformed previously published methods. For verification, we used UWF fundus images with NPA and used Bland-Altman plots to compare estimated NPA with the ground truth in FA, which demonstrated that bias between the eNPA and ground truth was smaller than 10% of the confidence limits zone and that the number of outliers was less than 10% of observed paired images. The accuracy of the model was also tested on an external dataset from another institution, which confirmed the generalization of the model. For validation, we employed a contingency table for ROC analysis to judge the sensitivity and specificity of the estimated-NPA (eNPA). The results demonstrated that the sensitivity and specificity ranged from 83.3-87.0% and 79.3-85.7%, respectively. In conclusion, we developed an AI model capable of estimating NPA size from only an UWF image without angiography using PraNet-based deep learning. This is a potentially useful tool in monitoring eyes with ischemic retinal diseases.

SUBMITTER: Inoda S

PROVIDER: S-EPMC9759556 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

Inoda Satoru S Takahashi Hidenori H Yamagata Hitoshi H Hisadome Yoichiro Y Kondo Yusuke Y Tampo Hironobu H Sakamoto Shinichi S Katada Yusaku Y Kurihara Toshihide T Kawashima Hidetoshi H Yanagi Yasuo Y

Scientific reports 20221217 1

We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and optimized hyperparameters in order to minimize validation loss. As expected, the resultant PraNet-based deep-learning model outperformed previously published methods. For verification, we used UWF fundus ...[more]

PMID: 36528737

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Project description:PurposeTo compare the relative number of retinal pixels and retinal area imaged using the Optos P200DTx (Optos PLC) and Zeiss Clarus 500 (Carl Zeiss Meditec AG) ultra-widefield (UWF) fundus cameras.DesignSingle-center retrospective cross-sectional analysis.ParticipantsSeventy-eight eyes of 46 patients.MethodsEyes were imaged with Optos P200DTx, single-capture, and Zeiss Clarus 500, 2 capture montages when possible, UWF fundus cameras. Relative number of pixels encompassing all foveal-centered retinal quadrants were measured. Retinal area was measured with Zeiss Clarus 500 images that were registered to the Optos P200DTx images. Patients and technicians were asked for preferences between the machines. Imaging session times were recorded.Main outcome measuresRelative number of retinal pixels and retina area captured by each fundus camera.ResultsOptos P200DTx consistently captured more relative pixels compared with Zeiss Clarus 500: 510.4 versus 355.6 (P < 0.001) in total with a similarly statistically significant trend in all 4 quadrants (P < 0.001 for each). For area calculation, 70 of the 78 images achieved successful registration. Optos captured a larger total retinal area: 765.6 versus 566.5 mm2 (P < 0.001) with a similarly statistically significant trend in all 4 quadrants. In the subset of 52 of 70 registered and montaged Zeiss Clarus 500 images, similar results were found. For peripheral pathologic features, Optos P200DTx captured unique findings in 28 images, and Zeiss Clarus 500 captured unique findings 1 image (P < 0.001). Among the 48 imaging sessions in which technicians preferred Optos P200DTx for 28 sessions (58%) and Zeiss Clarus 500 for 20 (42%; P = 0.15). Among patients who responded with a preference, 24 preferred Optos P200DTx and 20 preferred Zeiss Clarus 500 (P = 0.52). Average imaging session time was 4.6 minutes (standard deviation, 3.0 minutes) for Optos P200DTx and 5.2 minutes (standard deviation, 3.0 minutes) for Zeiss Clarus 500 (P = 0.17).ConclusionsIn the current study, the Optos P200DTx captured statistically significantly more retinal area in all 4 quadrants compared with the Zeiss Clarus 500. No statistically significant difference was found in patient or technician preference or image acquisition time between devices.

Project description:IntroductionCompared with traditional fundus examination techniques, ultra-widefield fundus (UWF) images provide 200° panoramic images of the retina, which allows better detection of peripheral retinal lesions. The advent of UWF provides effective solutions only for detection but still lacks efficient diagnostic capabilities. This study proposed a retinal lesion detection model to automatically locate and identify six relatively typical and high-incidence peripheral retinal lesions from UWF images which will enable early screening and rapid diagnosis.MethodsA total of 24,602 augmented ultra-widefield fundus images with labels corresponding to 6 peripheral retinal lesions and normal manifestation labelled by 5 ophthalmologists were included in this study. An object detection model named You Only Look Once X (YOLOX) was modified and trained to locate and classify the six peripheral retinal lesions including rhegmatogenous retinal detachment (RRD), retinal breaks (RB), white without pressure (WWOP), cystic retinal tuft (CRT), lattice degeneration (LD), and paving-stone degeneration (PSD). We applied coordinate attention block and generalized intersection over union (GIOU) loss to YOLOX and evaluated it for accuracy, sensitivity, specificity, precision, F1 score, and average precision (AP). This model was able to show the exact location and saliency map of the retinal lesions detected by the model thus contributing to efficient screening and diagnosis.ResultsThe model reached an average accuracy of 96.64%, sensitivity of 87.97%, specificity of 98.04%, precision of 87.01%, F1 score of 87.39%, and mAP of 86.03% on test dataset 1 including 248 UWF images and reached an average accuracy of 95.04%, sensitivity of 83.90%, specificity of 96.70%, precision of 78.73%, F1 score of 81.96%, and mAP of 80.59% on external test dataset 2 including 586 UWF images, showing this system performs well in distinguishing the six peripheral retinal lesions.ConclusionFocusing on peripheral retinal lesions, this work proposed a deep learning model, which automatically recognized multiple peripheral retinal lesions from UWF images and localized exact positions of lesions. Therefore, it has certain potential for early screening and intelligent diagnosis of peripheral retinal lesions.

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Dataset Information

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

Publications

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.

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